{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c67f16fe",
   "metadata": {},
   "source": [
    "# python 作业\n",
    "## 作业要求\n",
    "绘制 sin 函数波形\n",
    "使用了库 numpy\n",
    "和 pylab\n",
    "## 代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5f8ecbd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pylab as pl\n",
    "x = np.linspace(0, 4*np.pi, 100)\n",
    "pl.plot( x, np.cos(x))\n",
    "pl.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
